Federal Reserve Economic Data

How much are members of the US military paid compared with civilian employees of the federal government? In short, they’re paid less.

Our FRED graph above plots the average compensation of military personnel as a percentage of the average compensation of civilian employees of the federal government. Let’s look at the percentages in relation to some geopolitical and military events along this 25-year timeline:

Before the 9/11/2001 attacks, an average member of the military earned about 70% of what an average civilian government employee earned. From 2001 to 2009, the average military paycheck became increasingly comparable to a civilian paycheck, peaking in 2009 at 94%.

Operation Iraqi Freedom ended in 2010, and Osama bin Laden was killed in 2011. These dates coincide with the start of a decline in the pay of military personnel relative to the pay of civilian government employees. The decline brought a military paycheck down to around 80% of a civilian paycheck in 2017. Since 2017, there’s been a slow increase again.

Of course, the graph doesn’t explain why the military are paid less than the civilian employees of the US government. But it does show that increased demand for military services abroad and the likely heightened risk faced by the military during the 2001-2009 period coincided with increased compensation relative to the civilian. And this is exactly what one would expect from a basic supply-and-demand analysis.

How this graph was created: Search FRED for and select “Compensation of employees: Federal general government: Military (W4080C0A144NBEA).” Click on “Edit Graph” and, in sequence, add the following series to the graph: Full-time equivalent employees: Federal general government: Military (B4380C0A173NBEA), Compensation of employees: Federal general government: Civilian (B4079C0A144NBEA), Full-time equivalent employees: Federal general government: Civilian (B4379C0A173NBEA). In the “Formula” field, enter (a/b) / (c/d) * 100 and click “Apply formula.”

Suggested by Guillaume Vandenbroucke.

There’s been renewed attention to trade policy, tariff measures, and bilateral agreements, including trade agreements between the United States and India. Our FRED graph above shows monthly US exports and imports to and from India over the past four decades. Both imports (orange line) and exports (blue line) have gradually risen since the 1990s.

Yet, they are a small share of total US imports and exports, as shown by our second FRED graph, below. As of January 2026, exports to India accounted for about 2.5% of total US exports, and imports from India accounted for 3% of total US imports.

Data in FRED also allow us to see the geographic distribution of trade with India across US states. Our FRED map below illustrates the value of exports to India as of 2022. States shaded in dark green were the top exporters, with export values between $776.8 million and $5.72 billion. Most of these states lie along the East and West coasts, with some high-exporting states in the Midwest (Illinois) and the South (Louisiana, Florida, and Georgia).

How these graphs and maps were created: First graph: Search FRED for “EXP5330” and select “U.S. Exports of Goods by F.A.S. Basis to India.” In the “Edit Graph” panel, open the “Add Line” tab to search for “IMP5330” and select “U.S. Imports of Goods by Customs Basis from India.” Click “Add data series.” Open the “Format” tab to change the color of the second line to orange and the line style to solid. Second graph: Search FRED for “EXP0015” and select “U.S. Exports of Goods by F.A.S. Basis to World.” In the “Edit Graph” panel, open the “Edit Lines” tab. Scroll down to “Customize data” to search for “EXP5330” and add “U.S. Exports of Goods by F.A.S. Basis to India.” In the “Formula” tab, apply formula b/a to get the share of exports to India in total U.S. exports. To calculate the share of imports from India in total U.S. imports, open the “Add Line” tab and search for “IMP0015” and select “U.S. Imports of Goods by Customs Basis from World.” In the “Edit Graph” panel, open the “Edit Lines” tab. Scroll down to “Customize data” to search for “IMP5330” and select “U.S. Imports of Goods by Customs Basis from India.” Apply the same formula as line 1. Then open the “Format” tab and change the color of the second line to orange and the line style to solid. Ensure that all four datasets are of the same format. Map: Search FRED for “Value of exports to India from” and click on the first option. Click “View Map” and, in the “Edit Map” section, change the number of color groups to 5 and choose the fractile method.

Suggested by Revathy Ramchandran and B. Ravikumar.

On the first Friday of the month, the Bureau of Labor Statistics releases data on the prior month’s job growth and the unemployment rate. It’s one of the most anticipated data releases. Financial market participants pay close attention to see whether the change in jobs is consistent with forecasters’ expectations. For example, the February 2026 report showed the economy shed 92,000 jobs, while forecasters anticipated modest gains of around 50,000 jobs, a difference of 142,000 jobs.

At first glance that may seem like a huge discrepancy, but putting these numbers in a broader context paints a slightly different picture.

First, the data are from a sample of businesses.

These economic statistics are derived from a sample of about 120,000 businesses. Because it is a sample, these statistics have what are known as confidence intervals.* The BLS reported that, for the recent 92,000 jobs lost, the 90% confidence interval ranged from a possible loss of -214,000 jobs to a possible gain of 30,300 jobs.

Second, the labor market is dynamic.

Underlying these headline statistics is a very dynamic labor market where millions of people are changing jobs every month. The FRED graph above plots total nonfarm hires and separations over the past 5 years. The values range from a low of around 5,000,000 to high of around 7,000,000. These statistics come from another BLS report called the Job Openings and Labor Turnover survey, which surveys around 20,000 establishments and is released toward the end of the month.

The second graph shows the difference between hires and separations along with changes in nonfarm payroll employment (i.e., the headline job growth number). For example, the “strong” January jobs report showed the economy adding 126,000 jobs, while the JOLTS data above indicate 5,105,000 people left their employer and 5,294,000 were hired by a new employer, for a net difference of 189,000. Given the confidence intervals of these two surveys, these numbers are not statistically different from one another.

So, when reading the headlines about changes in employment in the thousands, it’s useful to keep in mind a few facts about the millions of jobs in the labor market.

1. These are estimates with confidence intervals, which imply a wide range of possible outcomes.
2. The employment base is almost 160,000,000 nonfarm employees.
3. There are about 5,000,0000 separations and hires each month, and the headline jobs number is the difference between these numbers.

*A note about confidence intervals: A recent On the Economy blog post discusses the confidence intervals and statistical framework related to the unemployment rate. A recent FRED Blog post discusses confidence intervals related to US poverty estimates: “In short, a confidence interval is the level of certainty about the accuracy of the estimate. The Census Bureau routinely employs a 90% confidence interval for its estimates. As they explain, a 90% confidence interval provides a level of certainty that, if you measure poverty using the same procedure multiple times, the estimated value will be within the range 90 out of 100 times.”

How these graphs were created: Search FRED for and select “Total Separations: Total Nonfarm.” Click on the “Edit Graph” button in the top right corner and open the “Add Line” tab. Search for “JTSHIL” and choose “Hires: Total Nonfarm.” Adjust the date range to start January 1, 2021, to see the past 5 years. For the second graph, again search for and select “JTSHIL.” Click on “Edit Graph,” search for “JTSTSL” in the Customize data portion, and choose “Total Separations: Total Nonfarm.” Apply formula a-b. Open the “Add Line” tab and search for “All Employees, Total Nonfarm.” Change units to “Change, Thousands of Persons.” Edit the timeframe to start January 1, 2021, to see the past 5 years.

Suggested by John Fuller and Charles Gascon.